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Publication numberUS20030093366 A1
Publication typeApplication
Application numberUS 10/046,945
Publication date15 May 2003
Filing date14 Jan 2002
Priority date13 Nov 2001
Also published asUS8386378, US8458082, US20110106693, US20130275293
Publication number046945, 10046945, US 2003/0093366 A1, US 2003/093366 A1, US 20030093366 A1, US 20030093366A1, US 2003093366 A1, US 2003093366A1, US-A1-20030093366, US-A1-2003093366, US2003/0093366A1, US2003/093366A1, US20030093366 A1, US20030093366A1, US2003093366 A1, US2003093366A1
InventorsSteven Halper, Stephen Hourigan, Constance Wilson
Original AssigneeHalper Steven C., Wilson Constance A., Hourigan Stephen M.
Export CitationBiBTeX, EndNote, RefMan
External Links: USPTO, USPTO Assignment, Espacenet
Automated loan risk assessment system and method
US 20030093366 A1
Abstract
An automated loan risk assessment system and method are described. The system is adapted to receive information about a loan or an insurance application requesting insurance to cover same. The system calculates a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, a credit risk factor and a property valuation risk factor. The risk score can be used by a loan service provider in deciding whether or not to fund or insure the loan.
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Claims(56)
We claim:
1. An automated loan risk assessment system, comprising:
means for receiving information about a loan; and
means for calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether or not to fund or insure the loan.
2. The automated loan risk assessment system of claim 1, wherein the risk calculation means comprises:
means for calculating a fraud risk score;
means for calculating an underwriting risk score; and
means for calculating a property valuation score, wherein the risk score for the loan is based on at least two of the fraud risk score, the underwriting risk score and the property valuation risk score.
3. The automated loan risk assessment system of claim 2, wherein the fraud risk score calculation means comprises:
means for storing general information about borrowers and properties; and
means for detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof.
4. The automated loan risk assessment system of claim 3, further comprising means for calculating a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores.
5. The automated loan risk assessment system of claim 3, further comprising means for determining one or more steps needed to resolve the one or more detected variances.
6. The automated loan risk assessment system of claim 3, further comprising means for tracking the status of the one or more detected variances.
7. The automated loan risk assessment system of claim 2, wherein the underwriting risk score calculation means comprises means for obtaining the underwriting risk score from an underwriting risk score provider, and wherein the property valuation risk score calculation means comprises means for obtaining a property valuation risk score from a property valuation score provider.
8. The automated loan risk assessment system of claim 2, further comprising means for converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score.
9. The automated loan risk assessment system of claim 8, wherein the means for converting comprises means for weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level of risk associated therewith such that the risk score is based on the weights assigned thereto.
10. The automated loan risk assessment system of claim 8, wherein the means for converting comprises converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and wherein the risk score represents an average of the compatible scores.
11. The automated loan risk assessment system of claim 1, further comprising means for assigning a risk category to the loan based on the risk score.
12. The automated loan risk assessment system of claim 1, wherein the loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which a loan is subject, and wherein the means for calculating a risk score comprises means for calculating a risk score for the claim based on a plurality of risk factors including at least one of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.
13. The automated loan risk assessment system of claim 1, further comprising means for interfacing at least one pricing scheme of a loan service provider such that a loan or an insurance policy for a loan can be automatically priced based on the risk score calculated therefor.
14. The automated loan risk assessment system of claim 1, wherein the risk score is based on a combination of the fraud risk score factor, the underwriting risk factor and the property valuation risk factor.
15. An automated loan risk assessment system, comprising:
a mechanism adapted to receive information about a loan; and
a mechanism adapted to calculate a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether or not to fund or insure the loan.
16. The automated loan risk assessment system of claim 15, wherein the risk calculation mechanism comprises:
a mechanism adapted to calculate a fraud risk score;
a mechanism adapted to calculate an underwriting risk score; and
a mechanism adapted to calculate a property valuation score, wherein the risk score for the loan is based on at least two of the fraud risk score, the underwriting risk score and the property valuation risk score.
17. The automated loan risk assessment system of claim 16, wherein the fraud risk score calculation mechanism comprises:
a mechanism adapted to store general information about borrowers and properties; and
a mechanism adapted to detect one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof.
18. The automated loan risk assessment system of claim 17, further comprising a mechanism adapted to calculate a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores.
19. The automated loan risk assessment system of claim 17, further comprising a mechanism adapted to determine one or more steps needed to resolve the one or more detected variances.
20. The automated loan risk assessment system of claim 17, further comprising means for tracking the status of the one or more detected variances.
21. The automated loan risk assessment system of claim 16, wherein the underwriting risk score calculation mechanism comprises a mechanism adapted to obtain the underwriting risk score from an underwriting risk score provider, and wherein the property valuation risk score calculation mechanism comprises a mechanism adapted to obtain a property valuation risk score from a property valuation score provider.
22. The automated loan risk assessment system of claim 16, further comprising a mechanism adapted to convert at least one of the fraud risk score, the underwriting risk score and the property valuation risk score.
23. The automated loan risk assessment system of claim 22, wherein the converting mechanism comprises a mechanism adapted to weight at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level of risk associated therewith such that the risk score is based on the weights assigned thereto.
24. The automated loan risk assessment system of claim 22, wherein the converting mechanism is adapted to convert at least one of the fraud risk score, the underwriting score and the property valuation risk score such that all of the scores are compatible, and wherein the risk score represents an average of the compatible scores.
25. The automated loan risk assessment system of claim 15, further comprising a mechanism adapted to assign a risk category to the loan based on the risk score.
26. The automated loan risk assessment system of claim 15, wherein the loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which a loan is subject, and wherein the mechanism for calculating a risk score comprises a mechanism adapted to calculate a risk score for the claim based on a plurality of risk factors including at least one of a fraud risk factor, an underwriting risk factor and a property valuation risk factor whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.
27. The automated loan risk assessment system of claim 15, further comprising a mechanism adapted to interface at least one pricing scheme of a loan service provider such that a loan or an insurance policy for a loan can be automatically priced based on the risk score calculated therefor.
28. The automated loan risk assessment system of claim 15, wherein the risk score is based on a combination of the fraud risk score, the underwriting risk score and the property valuation risk score.
29. A computer-readable medium whose contents cause a computer system to assess the risk associated with funding or insuring a loan by performing the steps of:
receiving information about a loan; and
calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, a credit risk factor and a property valuation risk factor.
30. The computer-readable medium of claim 29, wherein the step of calculating the risk score further comprises the steps of:
calculating a fraud risk score;
calculating an underwriting risk score; and
calculating a property valuation score, wherein the risk score for the loan is based on the fraud risk score, the underwriting risk score and the property valuation risk score.
31. The computer-readable medium of claim 30, wherein the step of calculating the fraud risk score comprises:
storing general information about borrowers and properties; and
detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof.
32. The computer-readable medium of claim 31, further comprising the step of calculating a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores.
33. The computer-readable medium of claim 31, further comprising the step of determining one or more steps needed to resolve the one or more detected variances.
34. The computer-readable medium of claim 31, further comprising the step of tracking the status of the one or more detected variances.
35. The computer-readable medium of claim 30, wherein the step of calculating the underwriting risk score comprises obtaining the underwriting risk score from an underwriting risk score provider, and wherein the step of calculating the property valuation risk score comprises obtaining a property valuation risk score from a property valuation score provider.
36. The computer-readable medium of claim 30, further comprising the step of converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score.
37. The computer-readable medium of claim 36, wherein the step of converting comprises weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level or risk associated therewith such that the risk score is based on the weights assigned thereto.
38. The computer-readable medium of claim 36, wherein the step of converting comprises converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and averaging the compatible scores.
39. The computer-readable medium of claim 29, further comprising the step of assigning a risk category to the loan based on the risk score.
40. The computer-readable medium of claim 29, wherein the loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which a loan is subject, and wherein the medium further comprises the step of calculating a risk score for the claim based on a plurality of risk factors including at least one of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.
41. The computer-readable medium of claim 29, further comprising the step of interfacing at least one pricing scheme of a loan service provider such that a loan or an insurance policy can be automatically priced based on the risk score calculated therefor.
42. The computer-readable medium of claim 29, wherein the risk score is based on a combination of the fraud risk score, the underwriting risk score and the property valuation risk score.
43. A computer-implemented method of assessing the risk associated with the funding or insuring of a loan, comprising:
receiving information about a loan; and
calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor.
44. The computer-implemented method of claim 43, wherein the step of calculating the risk score further comprises the steps of:
calculating a fraud risk score;
calculating an underwriting risk score; and
calculating a property valuation score, wherein the risk score for the loan is based on the fraud risk score, the underwriting risk score and the property valuation risk score.
45. The computer-implemented method of claim 44, wherein the step of calculating the fraud risk score comprises:
storing general information about borrowers and properties; and
detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof.
46. The computer-implemented method of claim 45, further comprising the step of calculating a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores.
47. The computer-implemented method of claim 45, further comprising the step of determining one or more steps needed to resolve the one or more detected variances.
48. The computer-implemented method of claim 45, further comprising the step of tracking the status of the one or more detected variances.
49. The computer-implemented method of claim 44, wherein the step of calculating the underwriting risk score comprises obtaining a credit risk score from a credit risk score provider, and wherein the step of calculating the property valuation risk score comprises obtaining a property valuation risk score from a property valuation score provider.
50. The computer-implemented method of claim 44, further comprising the step of converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score.
51. The computer-implemented method of claim 50, wherein the step of converting comprises weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level of risk associated therewith such that the risk score is based on the weights assigned thereto.
52. The computer-implemented method of claim 50, wherein the step of converting comprises converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and averaging the compatible scores.
53. The computer-implemented method of claim 43, further comprising the step of assigning a risk category to the loan based on the risk score.
54. The computer-implemented method of claim 43, wherein the loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which the loan is subject, and wherein the step of calculating a risk score comprises calculating a risk score for the claim based on a plurality of factors including at least one of a fraud risk factor, an underwriting risk factor, and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.
55. The computer-implemented method of claim 44, further comprising the step of interfacing at least one pricing scheme of a loan service provider such that a loan or insurance policy can be automatically priced based on the risk score calculated therefor.
56. The computer-implemented method of claim 44, wherein the risk score is based on a combination of the fraud risk score, the underwriting risk score, and the property valuation risk score.
Description
DETAILED DESCRIPTION OF THE INVENTION

[0032]FIG. 1 shows a block diagram of a system 10 in accordance with one embodiment of the present invention; namely the assessment of risk associated with insuring a mortgage based on a plurality of risk factors including without limitation a fraud risk factor, an underwriting risk factor and a property valuation risk factor. While the system 10 will be described in connection with the insurance of a mortgage, it can be appreciated that the system 10 can be applied to the funding or insuring of any type of loan. The system 10 consists of a plurality of databases for storing a plurality of different types of information. In particular, a database 12 stores a variety of specific information related to the loan, including without limitation information about the borrower and the subject property. Such information may come from a variety of documents including without limitation an insurance application 1, an Escrow Waiver 3, an Adjustable Rate Note 5, an Itemization of Amount Financed 7, a U.S. Department of Housing and Urban Development (HUD) I Settlement statement 9, an Adjustable Rate Note 11, a Special Closing Instructions document 13, a Truth-in-Lending statement 15, a loan document worksheet 17, a Deed of Trust 19, and a residential loan application 21 (also known as a “1003”). To the extent the system 10 is also or alternatively being used to assess the risk associated with insuring a loan, the loan information may include information related to the insurance of the loan. Such information may come from the insurer and the insurance application.

[0033] A database 14 stores general information related to borrowers, lenders, insurers, properties and any other aspect of the loan. Borrower information may include personal information about the borrower such as his or her name, address, and Social Security number. Lender information may include the lender's name, address and lending history. Property information may include addresses and appraisal values. This general information can come internally from the operator of the system 10, and/or from one or more third party or external database sources. For example, property information could come from such third party sources as International Data Management Corporation (IDM), Data Quick and Management Risk Assessment Corporation (MRAC), and Accumail United States Postal Service National Database. Borrower and lender information could come from third party sources, such as Trans Union, Equifax, Lexis Nexis, Acxiom, Info USA and Dunn and Bradstreet.

[0034] The loan information and the general information are stored in a database server 16, which includes communication software for communicating with third party or external databases not stored therein. It can be appreciated, however, that the loan information and the general information could be each stored in a separate database server or stored in various combinations thereof as needed. In one embodiment, the server database 16 is a Dell Power-Edge 2400 running Sequel Server 2000 software in a Windows 2000 operating system environment. In a preferred embodiment, two database servers are provided for load balancing and redundancy.

[0035] The loan information may be input into system 10 for storage in loan database 12 via input devices 18. While input devices 18 as shown are personal computers, they can be any type of device that allows the input of data. Specifically, the insurer logs on to system 10 through an input device 18, whereupon several screens such as screen 50 shown in FIG. 3, are displayed. Each screen 50 may include one or more fields in which the loan information can be input. For example, screen 50 includes a General Information section 52 in which general information about the borrower can be input, such as last name, middle name, first name, Social Security number, phone number, age and citizenship. Current residence section 54 allows the insurer to input information related to the borrower's current residence. Employer Information sections 56 and 58 allow the insurer to input information related to a borrower's current and previous employers. Once the information has been input, the insurer can save it by clicking on the “Save Data” button 60. If the insurer does not wish to save the information, he or she can simply click the “Cancel” button 62. Similar screens are displayed to the insurer until all of the necessary loan information has been input. Once input, the loan information can be downloaded to loan database 12.

[0036] Input devices 18 are shown as being located at the insurer's establishment such that the loan information is input directly by the insurer and then simply downloaded to database server 16 for storage in the loan database 12. The insurer may in turn use a document preparation company or rely on the lender to input and download some or all of the loan information directly for storage in loan database 12. Alternatively, the loan information can be sent to the operator of the system 10 to be input via one or more input devices 20 connected either directly or remotely to an application server 22. Such input devices 20 may then also be used to input any general information to be stored in general database 14. The loan information may be input to system 10 by a lender in the same manner as described above with respect to the insurer. One or more of the input devices 18 or 20 may be connected to a printer 24 for printing reports generated by the system 10.

[0037] Application server 22 is responsible for processing the loan information associated with each loan or insurance application to assess the level of risk associated with the funding and/or insuring of the loan, respectively. Application server 22 includes memory (not shown) for storing the program or programs necessary for assessing such risk as will be further discussed herein. Application server 22 interfaces with the input devices 18, underwriting scoring systems 30, and property valuation systems 32 through server 28. The connection between server 28 and input devices 18, underwriting scoring systems 30, and property valuation systems 32 can be via any communication network such as the telephone network, a satellite network, a cable network or any other communications network capable of transmitting information across it. Server 28 includes communication software to allow it to communicate with input devices 18, underwriting scoring systems 30, and property valuation systems 32. In one embodiment, application server 22 and server 28 are Dell Power-Edge 1550 servers running Microsoft Internet Information Services (IIS) Server v5.0 software under a Windows 2000 advanced server operating system. In a preferred embodiment, server 28 is a web server that allows system 10 to be implemented through a website accessible via the Internet. However, it can be appreciated that any type of server having the necessary processing capabilities and storage capacity may be used. In a preferred embodiment, application server 22 and server 28 are provided in duplicate for load balancing and redundancy.

[0038] The process of assessing the level of risk associated with insuring a loan will be described with reference to FIGS. 2, 4 and 5. For exemplary purposes, this process will be discussed in connection with a system 10 that is web-based and accessed by a mortgage insurer. It can be appreciated, however, that the system 10 need not be web-based to operate, and any loan service provider with authorized access to the system 10 and who desires the ability to automatically assess the risk associated with the funding and/or insuring of a loan may use the system 10.

[0039]FIG. 2 illustrates the process of assessing the risk of insuring a mortgage based on the fraud risk factor. At 100, information about the loan requesting to be insured is input. At 102, application server 22 checks this loan information to determine if there are any variances or differences among the loan information stored in loan database 12 or between the loan information stored in loan database 12 and the general information stored in general information database 34. For example, in the case of falsified identity, the social security number provided is checked to see if it corresponds to someone who has died, if it has been reported stolen, if it was issued prior to the borrower's birth year, or it if does not match the borrower's age. If no variances are found, at 105 the system 10 scores the loan accordingly.

[0040] If one or more variances are found, at 106, the system 10 preferably scores each variance based on the degree thereof. In one embodiment, the score is a numeric value such that the higher the degree of variance, the lower the score. For example, a discrepancy in the borrower's address may be scored lower (i.e., worse) than a discrepancy in the employer's address. It can be appreciated, however, that a reverse scoring system could be used whereby a higher degree of variance results in a higher score. It can also be appreciated that any type of scoring system indicative of the severity of the risk associated with the detected variance, including a non-numeric one, could be used. For example, each detected variance can be assigned a specific weight or grade based on its severity. Likewise, the system 10 can calculate a fraud score (as discussed below) based on the type, number and severity of the detected variances rather than scoring each variance separately.

[0041] At 108, the system 10 calculates a fraud score based on the sum of the scores of each detected variance and at 110, assigns the loan a risk category based on the fraud score. In one embodiment, a total score ranging between 600 and 1000 results in a “Pass” score, a total score ranging between 401 and 599 results in a “High” score, and a total score ranging between 0 and 400 results in an “Investigate” score. A Pass score means that there were no or minimal variances detected in connection with the loan information and that therefore, there is no actual fraud detected in connection with this loan. A High score means that the variances detected indicate a potential for fraud and that therefore while there is a relatively low level of risk of insuring the loan vis-à-vis fraud, the insurer may nevertheless want to further scrutinize the loan information. An Investigate score means that there is some aspect of the loan that is potentially fraudulent, but a greater level of risk than in the case of a High score. Again, any type of scoring system indicative of the risk associated with the loan information at issue may be used.

[0042] At 112, the system 10 determines what step or steps are needed to resolve any detected variances, and at 114, the system 10 notifies the user of the results. FIG. 5 shows one embodiment of how system 10 may notify a user of its results. Specifically, a screen 70 is displayed to the user on his or her input device 18. In section 72, identifying information about the loan is displayed, such as the name of the borrower and the loan number. In section 74, more detailed loan information is provided, such as for example the loan amount, the purchase price and the estimated/appraised value. Section 76 provides information from the insurance application. Section 76 provides a summary of the results of the insurance application as processed by system 10. At 78, the total fraud score is displayed, and at 80, the risk category (i.e., Pass, High or Investigate) is identified.

[0043] In the case of an Investigate status, section 82 identifies each variance or transgression and at 84, provides a description of the variance. In the example shown, the first transgression indicates that the property value exceeds its expected range. The second transgression indicates that the effective date on the insurance application does not reflect the loan closing date. At 86, the system 10 identifies any action that can be taken to resolve the transgression. A section 88 is also preferably provided which allows any additional comments regarding the transgression, as well as a section 90 which allows the user to track the status of a transgression and if and when it has been resolved. Alternatively, in the case where the insurance application is not being processed in real-time, notification can be sent to the user via e-mail, facsimile, telephone or any other known notification method.

[0044]FIG. 4 illustrates the process of assessing the level of risk associated with insuring a loan vis-à-vis a combination of the fraud, underwriting and property value risk factors. In particular, at 200, information about the loan requesting to be insured is input into system 10. At 202, application server 22 checks this loan information to determine if there are any variances among the loan information stored in loan database 12 or between the loan information stored in loan database 12 and the general information stored in general database 34. If no variances are detected, at 204 the system 10 scores the loan. If one or more variances are detected, at 206, the system 10 scores each variance based on the degree thereof. As stated previously herein, any scoring mechanism may be used. At 208, the system 10 calculates a fraud score for each insurance application based on the sum of the scores of each detected variance. As previously mentioned, in the case where each detected variance is not individually scored, the fraud score is based on the number, type and severity of detected variances. At 210, the system 10 obtains an underwriting score from an underwriting scoring system 30. At 212, the system 10 obtains a property valuation score from a property valuation system 32. At 214, the system 10 calculates a combined score based on a combination of the fraud, underwriting and property valuation scores.

[0045] Step 214 is performed by combining the three scores based on each individual score and the level of risk associated therewith. For discussion purposes only, it will be assumed that the fraud and property valuation scores are Pass, High or Investigate, and the underwriting score is one generated from a Fannie Mae underwriting system which includes the following: approve/eligible, approve/ineligible, refer/eligible, refer/ineligible, refer with caution or out of scope (i.e., reject). It will also be assumed that the combined score calculated by the system 10 will be the same as that used by the underwriting scoring system.

[0046] In one embodiment, the incompatible scores are “converted” by system 10 by assigning a weight to each individual score vis-à-vis the other scores and its corresponding level of risk. For example, a fraud score of Investigate will always be weighted such that the combined score will always be an Out of Scope score regardless of the underwriting and property valuation scores. Likewise, a property valuation score of Investigate will also always be weighted such that the combined score will always be an Out of Scope score regardless of the fraud and property valuation scores. In the case where there are no Investigate scores but at least one of the fraud or property valuation scores is High, the combined score will be Refer with Caution. In general, the less risk associated with each score, the better the combined score.

[0047] Alternatively, one or more of the scores are converted into a score that is compatible with the other. For example, the numeric fraud score can be used as the scoring system for the combined score and the underwriting and property valuation scores can be converted to a similar numeric value representative thereof. One advantage of using the numeric scores is that the level of risk is more specific. For instance, while a score of 401 and a score of 599 would both be High, the score 401 represents a higher risk than the score 599. Under such a system, an approve/eligible score will have a higher (i.e., better) score than a refer with caution score. Each score can then be added together and an average score computed. It can be appreciated, that any scoring system can be used for the combined score and that any fraud, underwriting and/or property valuation scores not compatible therewith would need to be “converted” by system 10 before the combined score could be calculated.

[0048] At 216, the system 10 assigns a risk category to the loan based on the combined score. In a preferred embodiment, at 218, the system 10 determines the steps needed to resolve any detected variances. At 220, the system 10 notifies the user of the results, and at 222 the process ends.

[0049] While the system and method have been described with respect to the assessment of risk based on the fraud score by itself, and a combination of the fraud, underwriting and property valuation scores, it can be appreciated that the system and method of the present invention can incorporate any combination of these scores (i.e., fraud score plus underwriting score, fraud score plus property valuation score, or underwriting plus property valuation score). With such a system and method, a loan service provider can better assess the level of risk involved with funding or insuring the loan through one source.

[0050] The system and method of the present invention can also be used to assist insurers with the processing of claims associated with their insurance policies. The system is the same in structure as system 10 shown in FIG. 1, except that the loan database 12 includes information input by the insurer related to the claims and corresponding insurance policies at issue and each insured's payment history for the policy. An insurer can determine whether to accept or deny a claim depending on at least one of a fraud risk score, and underwriting risk score, a property valuation risk score or a combined score calculated by the system for the claim at issue.

[0051] Finally, the system and method of the present invention can also be used as an automatic risk-pricing tool to assist loan service providers with the pricing of loans and insurance policies, respectively. Specifically, since the combined score is representative of the risk associated with the loan or insurance application, it can be used to price the loan or insurance policy covering it. In particular, server 28 of FIG. 1 interfaces the lender's or insurer's pricing scheme (not shown), such that the loan or insurance policy at issue can be automatically priced out based on the combined score calculated therefor.

[0052] In view of the foregoing, it will be seen that the several advantages of the invention are achieved and attained. The embodiments were chosen and described in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and with various modifications as are suited to the particular use contemplated. As various modifications could be made in the constructions and methods herein described and illustrated without departing from the scope of the invention, it is intended that all matter contained in the foregoing description or shown in the accompanying drawings shall be interpreted as illustrative rather than limiting. Thus, the breadth and scope of the present invention should not be limited by any of the above-described exemplary embodiments, but should be defined only in accordance with the following claims appended hereto and their equivalents.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0026] The accompanying drawings, which are incorporated in and form a part of the specification, illustrate the embodiments of the present invention and together with the description, serve to explain the principles of the invention. In the drawings:

[0027]FIG. 1 is a block diagram of a loan risk assessment system in accordance with one embodiment of the present invention.

[0028]FIG. 2 is a flowchart illustrating one embodiment of the steps for assessing the risk associated with a loan based on fraud using the system of FIG. 1.

[0029]FIG. 3 shows one embodiment of an input screen display generated by the system and method of the present invention.

[0030]FIG. 4 is a flowchart illustrating one embodiment of the steps for assessing the risk associated with a loan based on a combination of fraud, underwriting and property valuation risk factors using the system of FIG. 1.

[0031]FIG. 5 shows one embodiment of a report generated by the system and method of the present invention.

FIELD OF THE INVENTION

[0002] This invention relates to an automated loan risk assessment system and method and in particular, an automated system and method of assessing risk with respect to a loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor.

BACKGROUND OF THE INVENTION

[0003] One of the American dreams is home ownership. However, according to the Mortgage Guaranty Insurance Company, “[r]elative to the growth in home prices over the last century, Americans are earning less and, as a result, saving less.” As a result, the down payment required to secure a mortgage often prevents young individuals just “starting out” from buying a home. Consequently, home mortgages having low down payments have become very popular. The less money a borrower has invested in a home, however, the greater the risk of default. Therefore, while there is some risk that a borrower may default with a conventional mortgage which typically requires a twenty percent (20%) down payment, this risk is increased for borrowers who are only putting down five percent (5%) or ten percent (10%). Low down payment mortgages, therefore, often require that the borrower obtain some type of mortgage insurance to protect the lender against loss if the borrower defaults on the mortgage. However, even with such protection, the lender typically is not able to recoup the entire amount of the mortgage.

[0004] Lenders and mortgage insurers try to minimize their exposure by obtaining information on borrowers indicative of their risk of defaulting on a mortgage, such as through credit reports or mortgage service systems such as The Mortgage Office, MORESERV and TRAKKER. There are also several existing consumer and mortgage scoring systems which generate underwriting scores to assist mortgage insurers in this regard, such as for example, the Fair Issac Consumer (FICO) score, the Private Mortgage Insurance (PMI) aura score, the United Guaranty ACUscore, and the ARCS subprime mortgage score.

[0005] None of these scoring tools, however, assess risk attributable to fraud (i.e., data integrity). For example, a lender may manipulate the loan information to qualify an otherwise unqualified borrower, or a borrower may falsify income or employment information in order to obtain the loan. To the extent fraudulent claims are not detected, the costs associated with paying them are ultimately borne by the consumer. According to a Sep. 26, 2001 article in Realty Times, reports of possible fraudulent activity in connection with a mortgage increased fifty-seven percent (57%) in the first quarter of this year.

[0006] Fraud can originate from numerous sources, such as lenders, borrowers, appraisers, title agents, real estate agents and builders. Fraud can be injected into the loan process in a number of ways, such as through the use of false credit histories, false income/employment information, falsified appraisals, inflated property values and false identification. For example, loan officers might fabricate pay stubs to help a borrower qualify for a loan that the borrower might not otherwise qualify for so that he or she can collect a commission. Likewise, a borrower might submit falsified tax returns to ensure he or she qualifies for the loan.

[0007] The potential for fraud increases as the number of parties involved in the transaction increases. Increases in mortgage fraud are also due to a number of other factors, such as (1) the creation of new and creative forms of financing, coupled with automated underwriting, (2)the increased availability of personal information via the Internet, and (3) the low-cost of computer equipment such as printers and copiers that produce high quality copies such that one can fabricate authentic-looking documents (i.e., pay stubs, tax returns).

[0008] Not only do fraudulent loans result in enormous financial losses, misrepresenting information on a loan application is illegal. Moreover, penalties for fraudulent lending violations include substantial monetary penalties such as repayment of twice the amount of all interest, fees, discounts and charges as well as court and attorney fees to the borrower. In addition, such violations can result in the temporary or permanent suspension of business privileges of the lender, such as the ability to sell to quasi-governmental agencies (e.g., Freddie Mac and Fannie Mae) or in secondary markets, or the ability to sell certain types of loans. In some cases, lenders can lose their licenses and face imprisonment. In the secondary market, purchasers and assignees can be held liable for all claims on loans in their possession. These costs are then often passed on to consumers in the form of higher loan costs, higher lending fees and higher interest rates.

[0009] Yet another risk associated with funding or insuring a loan relates to the accuracy of the valuation of the subject property. One of the most common problems associated with property valuations is known as property flipping. This practice involves a property that is bought and then resold (i.e., flipped) several times, each time at a falsely inflated price. The property is then sold to an unsuspecting mortgage company that pays much more for the property than its market value that can result in a substantial loss to the mortgage lender upon the reselling of the property. Typically, lenders use internal or third party property valuation models or tools such as AppIntell, Inc.'s ValVerify, Case Shiller Weiss' CASA, Solimar's Basis100 or First American's product suite which includes Value Point, Home Price Index, Assessed Value Model, AREA's, and Value Point Plus to analyze the value of the property provided in the loan documents and score it based on its accuracy. Such an analysis looks at factors like the value of other properties around the location of the subject property and the selling prices of comparable properties. This score is usually in the form of a value or grade representing a confidence level, which corresponds to a range of predicated value. For example, in the case of CASA, Grade A refers to a predicted value range within 6%, Grade B refers to a predicted value range between 6% and 8%, Grade C refers to a predicted value range of between 8% and 10%, Grade D refers to a predicted value range between 10% and 14%, and Grade E refers to a predicted value range between 14% and 20%. The bigger the discrepancy between the property value provided in the loan documents and the property value determined by such models or tools, the greater the risk in funding or insuring the loan.

[0010] Currently, fraud, underwriting and property valuation scoring systems originate from different sources. As a result, they are not compatible with each other. In other words, mortgage service providers must go to one company to have a risk assessment of the loan from an underwriting perspective, a different company to have a risk assessment of the loan from a fraud perspective, and possibly yet a different company to have a risk assessment of the loan from a property valuation perspective. This cumbersome process not only significantly delays the underwriting process, but also increases its costs tremendously. In fact, the single largest insurance policy acquisition cost in mortgage insurance is contract underwriting. Approximately half of loan public filings by private mortgage insurers in 2000 were referred to underwriters for manual review after the loan was scored vis-à-vis the borrower's credit history. Moreover, since the scores are not compatible, they cannot be combined into an overall score reflecting the level of risk of funding or insuring a loan based on at least two of the three scores. The potential cost and time savings as well as value of an automatic risk assessment system that takes into account risk from at least two of a fraud, underwriting and property valuation perspective all provided from one source is enormous.

[0011] There is, therefore, a need for an automated system and method that assesses the risk associated with funding or insuring a loan based on a plurality of risk factors.

BRIEF SUMMARY OF THE INVENTION

[0012] It is in view of the above problems that the present invention was developed. In particular, an automated loan risk assessment system is disclosed which comprises a mechanism for receiving information about a loan, and a mechanism for calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether or not to fund or insure the loan. In one embodiment, the risk score is based on a combination of the fraud risk score factor, the underwriting risk factor and the property valuation risk factor.

[0013] The risk calculation mechanism may further comprise a mechanism for calculating a fraud risk score, a mechanism for calculating an underwriting risk score, and a mechanism for calculating a property valuation score, wherein the risk score for the loan is based on at least two of the fraud risk score, the underwriting risk score and the property valuation risk score. The fraud risk score calculation mechanism comprises a mechanism for storing general information about borrowers and properties, and a mechanism for detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof. The system may further comprise a mechanism for determining one or more steps needed to resolve the one or more detected variances, a mechanism for tracking the status of the one or more detected variances, and/or a mechanism for assigning a risk category to the loan based on the risk score.

[0014] The underwriting risk score calculation mechanism comprises means for obtaining the underwriting risk score from an underwriting risk score provider, the property valuation risk score calculation mechanism comprises means for obtaining a property valuation risk score from a property valuation score provider. The system further comprises a mechanism for converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score. In one embodiment, the converting mechanism comprises a mechanism for weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level of risk associated therewith such that the risk score is based on the weights assigned thereto. In another embodiment, the mechanism for converting comprises a mechanism for converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and wherein the risk score represents an average of the compatible scores.

[0015] The loan information may include insurance information related to at least one insurance claim being asserted against an insurance policy to which a loan is subject, such that the mechanism for calculating a risk score comprises a mechanism for calculating a risk score for the claim based on a plurality of risk factors including at least one of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.

[0016] The system may further comprise a mechanism for interfacing at least one pricing scheme of a loan service provider such that a loan or an insurance policy for a loan can be automatically priced based on the risk score calculated therefor.

[0017] The present invention also discloses a computer-readable medium whose contents cause a computer system to assess the risk associated with funding or insuring a loan by performing the steps of receiving information about a loan, and calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, a credit risk factor and a property valuation risk factor. The step of calculating the risk score further comprises the steps of calculating a fraud risk score, calculating an underwriting risk score, and calculating a property valuation score, wherein the risk score for the loan is based on the fraud risk score, the underwriting risk score and the property valuation risk score. In one embodiment, the risk score is based on a combination of the fraud risk score, the underwriting risk score and the property valuation risk score.

[0018] The step of calculating the fraud risk score may comprise storing general information about borrowers and properties, and detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof. In one embodiment, the medium includes the step of calculating a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores. The medium may further include the steps of determining one or more steps needed to resolve the one or more detected variances, tracking the status of the one or more detected variances, and/or assigning a risk category to the loan based on the risk score.

[0019] The step of calculating the underwriting risk score may comprise obtaining the underwriting risk score from an underwriting risk score provider, and the step of calculating the property valuation risk score may comprise obtaining a property valuation risk score from a property valuation score provider. The medium may further comprise the step of converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score. In one embodiment, the step of converting comprises weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level or risk associated therewith such that the risk score is based on the weights assigned thereto. In another embodiment, the step of converting comprises converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and averaging the compatible scores.

[0020] The loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which a loan is subject, such that the medium further comprises the step of calculating a risk score for the claim based on a plurality of risk factors including at least one of a fraud risk factor, an underwriting risk factor and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.

[0021] The medium may further comprise the step of interfacing at least one pricing scheme of a loan service provider such that a loan or an insurance policy can be automatically priced based on the risk score calculated therefor.

[0022] The present invention also discloses a computer-implemented method of assessing the risk associated with the funding or insuring of a loan. The method comprises receiving information about a loan, and calculating a risk score for the loan based on a plurality of risk factors including at least two of a fraud risk factor, an underwriting risk factor and a property valuation risk factor. The step of calculating the risk score comprises the steps of calculating a fraud risk score, calculating an underwriting risk score, and calculating a property valuation score, wherein the risk score for the loan is based on the fraud risk score, the underwriting risk score and the property valuation risk score. In one embodiment, the risk score is based on a combination of the fraud risk score, the underwriting risk score, and the property valuation risk score. The step of calculating the fraud risk score comprises storing general information about borrowers and properties, and detecting one or more variances among the loan information or between the loan information and the general information, each variance having a certain degree, such that the fraud risk score is based on the detected variances and the degrees thereof. In one embodiment, the method further comprises the step of calculating a variance score for each detected variance based on the degree thereof, wherein the fraud risk score represents the sum of the variance scores. The method may further comprise the steps of determining one or more steps needed to resolve the one or more detected variances, tracking the status of the one or more detected variances, and/or assigning a risk category to the loan based on the risk score.

[0023] The step of calculating the underwriting risk score comprises obtaining a credit risk score from a credit risk score provider, and the step of calculating the property valuation risk score comprises obtaining a property valuation risk score from a property valuation score provider. The method may further comprise the step of converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score. In one embodiment, the step of converting comprises weighting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score based on the level of risk associated therewith such that the risk score is based on the weights assigned thereto. In another embodiment, the step of converting comprises converting at least one of the fraud risk score, the underwriting risk score and the property valuation risk score such that all of the scores are compatible, and averaging the compatible scores.

[0024] The loan information includes insurance information related to at least one insurance claim being asserted against an insurance policy to which the loan is subject, such that the step of calculating a risk score comprises calculating a risk score for the claim based on a plurality of factors including at least one of a fraud risk factor, an underwriting risk factor, and a property valuation risk factor, whereby the risk score can be used by a loan service provider in deciding whether to allow or deny the claim.

[0025] The method may further comprise the step of interfacing at least one pricing scheme of a loan service provider such that a loan or insurance policy can be automatically priced based on the risk score calculated therefor.

CROSS REFERENCE TO RELATED APPLICATION

[0001] This is a continuation-in-part of application Ser. No. 09/993072 entitled Predatory Lending Detection System and Method Therefor filed Nov. 13, 2001.

Patent Citations
Cited PatentFiling datePublication dateApplicantTitle
US4736294 *23 Jun 19875 Apr 1988The Royal Bank Of CanadaData processing methods and apparatus for managing vehicle financing
US5481647 *21 Sep 19932 Jan 1996Raff Enterprises, Inc.User adaptable expert system
US5673402 *17 Aug 199230 Sep 1997The Homeowner's Endorsement Plan IncorporatedComputer system for producing an illustration of an investment repaying a mortgage
US5870721 *15 Oct 19969 Feb 1999Affinity Technology Group, Inc.System and method for real time loan approval
US5878403 *12 Sep 19952 Mar 1999CmsiComputer implemented automated credit application analysis and decision routing system
US5966700 *23 Dec 199712 Oct 1999Federal Home Loan Bank Of ChicagoManagement system for risk sharing of mortgage pools
US6021397 *2 Dec 19971 Feb 2000Financial Engines, Inc.Financial advisory system
US6029149 *26 Apr 199922 Feb 2000The Golden 1 Credit UnionLender direct credit evaluation and loan processing system
US6105007 *5 May 199915 Aug 2000Affinity Technology Group, Inc.Automatic financial account processing system
US6112190 *19 Aug 199729 Aug 2000Citibank, N.A.Method and system for commercial credit analysis
US6185543 *15 May 19986 Feb 2001Marketswitch Corp.Method and apparatus for determining loan prepayment scores
US6233566 *18 Mar 199915 May 2001Ultraprise CorporationSystem, method and computer program product for online financial products trading
US6249775 *11 Jul 199719 Jun 2001The Chase Manhattan BankMethod for mortgage and closed end loan portfolio management
US6385594 *8 May 19987 May 2002Lendingtree, Inc.Method and computer network for co-ordinating a loan over the internet
US6587841 *30 Jun 19981 Jul 2003First American Credit Management Solutions, Inc.Computer implemented automated credit application analysis and decision routing system
US6985886 *24 Aug 200010 Jan 2006EverbankMethod and apparatus for a mortgage loan management system
US6993505 *20 Aug 199731 Jan 2006Citibank, N.A.Method and system for performing CRA, HMDA, and fair lending analysis and reporting for a financial institution
US7392216 *27 Sep 200024 Jun 2008Ge Capital Mortgage CorporationMethods and apparatus for utilizing a proportional hazards model to evaluate loan risk
US7395239 *19 Jul 19991 Jul 2008American Business FinancialSystem and method for automatically processing loan applications
US7406442 *11 Sep 200029 Jul 2008Capital One Financial CorporationSystem and method for providing a credit card with multiple credit lines
US7599879 *21 Mar 20016 Oct 2009Jpmorgan Chase Bank, National AssociationSyndication loan administration and processing system
US7680728 *6 May 200216 Mar 2010Mortgage Grader, Inc.Credit/financing process
US7742966 *7 Aug 199922 Jun 2010Marketcore.Com, Inc.Efficient market for financial products
US7818254 *21 Jun 199919 Oct 2010Juno Holdings, N.V.Application apparatus and method
US7873556 *18 Oct 200218 Jan 2011Charles Schwab & Co., Inc.System and method for margin loan securitization
US8015091 *21 Nov 20016 Sep 2011Clayton Fixed Income Services, Inc.Analyzing investment data
US20010029482 *9 Mar 200111 Oct 2001Integrate Online, Inc.Online mortgage approval and settlement system and method therefor
US20010037274 *13 Mar 20011 Nov 2001Douglas MonticcioloMethod of cost effectively funding a loan
US20010042785 *8 May 200122 Nov 2001Walker Jay S.Method and apparatus for funds and credit line transfers
US20010047326 *13 Mar 200129 Nov 2001Broadbent David F.Interface system for a mortgage loan originator compliance engine
US20020019804 *29 Jun 200114 Feb 2002Sutton Robert E.Method for providing financial and risk management
US20020040339 *2 Oct 20014 Apr 2002Dhar Kuldeep K.Automated loan processing system and method
US20020052835 *30 Apr 20012 May 2002Toscano Paul JamesOn line loan process
US20020099650 *15 Nov 200125 Jul 2002Cole James A.Method for automatically processing a financial loan application and the system thereof
US20020103750 *4 Oct 20011 Aug 2002Thomas HerzfeldRenewable repriced mortgage guaranty insurance
US20020116323 *16 Feb 200122 Aug 2002Schnall Peter A.Method and apparatus for providing loan information to multiple parties
US20020116327 *4 Dec 200122 Aug 2002Venkatesan SrinivasanSystem and methods for syndication of financial obligations
US20020198822 *21 Jun 200126 Dec 2002Rodrigo MunozMethod and apparatus for evaluating an application for a financial product
US20030050879 *28 Aug 200113 Mar 2003Michael RosenSystem and method for improved multiple real-time balancing and straight through processing of security transactions
US20030093346 *13 Nov 200115 May 2003Weber & Associates, Inc.Virtual financial aid office
US20030093365 *13 Nov 200115 May 2003Halper Steven C.Predatory lending detection system and method therefor
US20030093366 *14 Jan 200215 May 2003Halper Steven C.Automated loan risk assessment system and method
US20040054619 *18 Sep 200218 Mar 2004Watson Tamara C.Methods and apparatus for evaluating a credit application
US20040117302 *16 Dec 200217 Jun 2004First Data CorporationPayment management
US20070043654 *21 Feb 200122 Feb 2007Libman Brian LAutomated loan evaluation system
US20110106693 *27 Oct 20105 May 2011Halper Steven CAutomated Loan Risk Assessment System and Method
Referenced by
Citing PatentFiling datePublication dateApplicantTitle
US720373428 Dec 200110 Apr 2007Insurancenoodle, Inc.Methods and apparatus for selecting an insurance carrier for an online insurance policy purchase
US734042416 Dec 20034 Mar 2008Fannie MaeSystem and method for facilitating sale of a loan to a secondary market purchaser
US7386528 *31 May 200210 Jun 2008American Express Travel Related Services Company, Inc.System and method for acquisition, assimilation and storage of information
US7451095 *30 Oct 200211 Nov 2008Freddie MacSystems and methods for income scoring
US7546271 *16 Jul 20089 Jun 2009Choicepoint Asset CompanyMortgage fraud detection systems and methods
US7587348 *22 Sep 20068 Sep 2009Basepoint Analytics LlcSystem and method of detecting mortgage related fraud
US76102104 Sep 200327 Oct 2009Hartford Fire Insurance CompanySystem for the acquisition of technology risk mitigation information associated with insurance
US761026131 Oct 200727 Oct 2009American Express Travel Related Services Company, Inc.System and method for acquisition, assimilation and storage of information
US765359230 Dec 200526 Jan 2010Fannie MaeSystem and method for processing a loan
US76687693 Oct 200623 Feb 2010Basepoint Analytics, LLCSystem and method of detecting fraud
US768950313 Nov 200130 Mar 2010Interthinx, Inc.Predatory lending detection system and method therefor
US7693764 *16 Jul 20046 Apr 2010Federal Home Loan Mortgage CorporationSystems and methods for assessing property value fraud
US76937822 Aug 20056 Apr 2010Fannie MaeMethod and system for evaluating a loan
US770710315 Mar 200527 Apr 2010Arthur J PriestonSystem and method for rating lenders
US77115747 Oct 20034 May 2010Federal Home Loan Mortgage Corporation (Freddie Mac)System and method for providing automated value estimates of properties as of a specified previous time period
US77115844 Sep 20034 May 2010Hartford Fire Insurance CompanySystem for reducing the risk associated with an insured building structure through the incorporation of selected technologies
US7725386 *7 Mar 200525 May 2010Arthur J PriestonMethod for offering representation and warranty insurance for mortgage loans
US7739189 *19 Oct 200715 Jun 2010Fannie MaeMethod and system for detecting loan fraud
US774752623 Aug 200629 Jun 2010Fannie MaeSystem and method for transferring mortgage loan servicing rights
US7756778 *16 Dec 200413 Jul 2010Fannie MaeSystem and method for tracking and facilitating analysis of variance and recourse transactions
US775677914 Feb 200513 Jul 2010Fannie MaeSystem and method for determining compliance with a delegated underwriting and servicing agreement
US7783565 *7 Nov 200724 Aug 2010Fannie MaeMethod and system for assessing repurchase risk
US77881869 Mar 200531 Aug 2010Fannie MaeMethod and system for automated property valuation adjustment
US779716630 Oct 200214 Sep 2010Federal Home Loan Mortgage Corporation (Freddie Mac)Systems and methods for generating a model for income scoring
US7801808 *24 Mar 200521 Sep 2010Morgan StanleyDatabase structure for financial products with unique, consistent identifier for parties that assume roles with respect to the products and methods of using the database structure
US78096354 Aug 20065 Oct 2010Corelogic Information Solutions, Inc.Method and system for updating a loan portfolio with information on secondary liens
US781400829 Feb 200812 Oct 2010American Express Travel Related Services Company, Inc.Total structural risk model
US7835919 *12 Mar 200216 Nov 2010Freddie MacSystems and methods for home value scoring
US785351824 May 200514 Dec 2010Corelogic Information Solutions, Inc.Method and apparatus for advanced mortgage diagnostic analytics
US785352029 Feb 200814 Dec 2010American Express Travel Related Services Company, Inc.Total structural risk model
US787357030 Aug 201018 Jan 2011Corelogic Information Solutions, Inc.Method and system for updating a loan portfolio with information on secondary liens
US7877320 *7 Jul 201025 Jan 2011Fannie MaeSystem and method for tracking and facilitating analysis of variance and recourse transactions
US788589217 Aug 20108 Feb 2011Fannie MaeMethod and system for assessing repurchase risk
US789974128 Mar 20071 Mar 2011Bank Of America CorporationLoss impact tracking system and method
US790453215 May 20068 Mar 2011Insurancenoodle, Inc.Methods and apparatus for selecting an insurance carrier for an online insurance policy purchase
US791277326 Mar 200722 Mar 2011Sas Institute Inc.Computer-implemented data storage systems and methods for use with predictive model systems
US79662566 Oct 200821 Jun 2011Corelogic Information Solutions, Inc.Methods and systems of predicting mortgage payment risk
US79748546 Apr 20105 Jul 2011Federal Home Loan Mortgage Corporation (Freddie Mac)Systems and methods for retrospective home value scoring
US798712418 Aug 200526 Jul 2011Fannie MaeMethod of and system for evaluating an appraisal value associated with a loan
US80151336 Sep 20076 Sep 2011Sas Institute Inc.Computer-implemented modeling systems and methods for analyzing and predicting computer network intrusions
US80555187 Mar 20058 Nov 2011Arthur J PriestonMethod for handling claims arising under representation and warranty insurance for mortgage loans
US806523416 Jun 201122 Nov 2011Corelogic Information Solutions, Inc.Methods and systems of predicting mortgage payment risk
US809073416 Sep 20093 Jan 2012American Express Travel Related Services Company, Inc.System and method for assessing risk
US812192010 Aug 200921 Feb 2012Corelogic Information Solutions, Inc.System and method of detecting mortgage related fraud
US81905126 Sep 200729 May 2012Sas Institute Inc.Computer-implemented clustering systems and methods for action determination
US8244563 *31 Oct 200714 Aug 2012Fnc, Inc.Appraisal evaluation and scoring system and method
US8266050 *30 Jan 200711 Sep 2012Bank Of America CorporationSystem and method for processing loans
US827130319 Feb 201018 Sep 2012Hartford Fire Insurance CompanySystem for reducing the risk associated with an insured building structure through the incorporation of selected technologies
US827570019 Apr 201025 Sep 2012Prieston Arthur JLender rating system and method
US83119127 Mar 200513 Nov 2012Arthur J PriestonMethod for determining premiums for representation and warranty insurance for mortgage loans
US83466916 Sep 20071 Jan 2013Sas Institute Inc.Computer-implemented semi-supervised learning systems and methods
US8386378 *27 Oct 201026 Feb 2013Interthinx, Inc.Automated loan risk assessment system and method
US840186811 May 201019 Mar 2013Freddie MacSystem and method for providing an income score of an applicant based on an income score model
US840714930 Aug 201026 Mar 2013Fannie MaeMethod and system for automated property valuation adjustment
US842345130 Dec 200516 Apr 2013Fannie MaiSystem and method for processing a loan
US843363128 Jan 201130 Apr 2013Fannie MaeMethod and system for assessing loan credit risk and performance
US8458082 *14 Jan 20024 Jun 2013Interthinx, Inc.Automated loan risk assessment system and method
US845808329 Feb 20084 Jun 2013American Express Travel Related Services Company, Inc.Total structural risk model
US8489499 *13 Jan 201016 Jul 2013Corelogic Solutions, LlcSystem and method of detecting and assessing multiple types of risks related to mortgage lending
US849893130 Jul 201230 Jul 2013Sas Institute Inc.Computer-implemented risk evaluation systems and methods
US851586229 May 200920 Aug 2013Sas Institute Inc.Computer-implemented systems and methods for integrated model validation for compliance and credit risk
US8515863 *1 Sep 201020 Aug 2013Federal Home Loan Mortgage CorporationSystems and methods for measuring data quality over time
US8521631 *29 May 200927 Aug 2013Sas Institute Inc.Computer-implemented systems and methods for loan evaluation using a credit assessment framework
US8527401 *24 Oct 20053 Sep 2013The First American CorporationProduct, system and method for certification of closing and mortgage loan fulfillment
US855466621 Jun 20118 Oct 2013American Express Travel Related Services Company, Inc.Total structural risk model
US855466716 Feb 20128 Oct 2013American Express Travel Related Services Company, Inc.Total structural risk model
US856622816 Feb 201222 Oct 2013American Express Travel Related Services Company, Inc.Total structural risk model
US856622916 Feb 201222 Oct 2013American Express Travel Related Services Company, Inc.Total structural risk model
US862080116 Feb 201231 Dec 2013American Express Travel Related Services Company, Inc.Total structural risk model
US86396182 Jul 201328 Jan 2014Corelogic Solutions, LlcSystem and method of detecting and assessing multiple types of risks related to mortgage lending
US20030093366 *14 Jan 200215 May 2003Halper Steven C.Automated loan risk assessment system and method
US20060106690 *29 Oct 200418 May 2006American International Group, Inc.Lender evaluation system
US20060136330 *24 Oct 200522 Jun 2006Deroy Craig IProduct, system and method for certification of closing and mortgage loan fulfillment
US20080162224 *31 Oct 20073 Jul 2008Kathy CoonAppraisal evaluation and scoring system and method
US20090222373 *29 Feb 20083 Sep 2009American Express Travel Related Services Company, Inc.Total structural risk model
US20090222376 *29 Feb 20083 Sep 2009American Express Travel Related Services Company, Inc.Total structural risk model
US20090222378 *29 Feb 20083 Sep 2009American Express Travel Related Services Company, Inc.Total structural risk model
US20100274708 *5 May 200928 Oct 2010Allen Lewis JApparatus and method for creating a collateral risk score and value tolerance for loan applications
US20110106693 *27 Oct 20105 May 2011Halper Steven CAutomated Loan Risk Assessment System and Method
US20110137788 *4 Dec 20099 Jun 2011Merkle Robert ASystems and methods for evaluating the ability of borrowers to repay loans
US20110173116 *13 Jan 201014 Jul 2011First American Corelogic, Inc.System and method of detecting and assessing multiple types of risks related to mortgage lending
US20110295624 *25 May 20111 Dec 2011Underwriters Laboratories Inc.Insurance Policy Data Analysis and Decision Support System and Method
US20120303420 *13 Aug 201229 Nov 2012Kathy CoonBroker price opinion evaluation and scoring system and method
US20130018776 *13 Jul 201117 Jan 2013First American WaySystem and Method for Income Risk Assessment Utilizing Income Fraud and Income Estimation Models
US20130138554 *30 Nov 201130 May 2013Rawllin International Inc.Dynamic risk assessment and credit standards generation
US20130185101 *17 Jan 201318 Jul 2013American International Group, Inc.System, Method, and Computer Program Product for Underwriting Mortgage Loan Insurance
WO2004072771A2 *4 Feb 200426 Aug 2004Fidelity Nat Financial IncSystem and method for evaluating future collateral risk quality of real estate
WO2006104680A2 *8 Mar 20065 Oct 2006First American Real Estate SolMethod and apparatus for computing a loan quality score
WO2006127295A2 *11 May 200630 Nov 2006Christopher L CaganMethod and apparatus for advanced mortgage diagnostic analytics
WO2009021045A1 *6 Aug 200812 Feb 2009Noah Joseph BreslowSystem and method for repaying an obligation
Classifications
U.S. Classification705/38
International ClassificationG06Q40/00
Cooperative ClassificationG06Q40/02, G06Q40/025
European ClassificationG06Q40/02, G06Q40/025
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Effective date: 20040920
Owner name: APPINTELLIGENCE, INC.,MISSOURI
Free format text: CHANGE OF NAME;ASSIGNOR:APPINTELL, INC.;US-ASSIGNMENT DATABASE UPDATED:20100330;REEL/FRAME:15474/346
14 Jan 2002ASAssignment
Owner name: APPINTELL, INC., MISSOURI
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:HALPER, STEVEN C.;WILSON, CONSTANCE A.;HOURIGAN, STEPHENM.;REEL/FRAME:012508/0306
Effective date: 20020111